Load default model with --model flag

server.py

@@ -2,23 +2,19 @@ import os
 import re
 import time
 import glob
+from sys import exit
 import torch
+import argparse
 import gradio as gr
 import transformers
 from transformers import AutoTokenizer
 from transformers import GPTJForCausalLM, AutoModelForCausalLM, AutoModelForSeq2SeqLM, OPTForCausalLM, T5Tokenizer, T5ForConditionalGeneration, GPTJModel, AutoModel
 
-#model_name = "bloomz-7b1-p3"
-#model_name = 'gpt-j-6B-float16'
-#model_name = "opt-6.7b"
-#model_name = 'opt-13b'
-model_name = "gpt4chan_model_float16"
-#model_name = 'galactica-6.7b'
-#model_name = 'gpt-neox-20b'
-#model_name = 'flan-t5'
-#model_name = 'OPT-13B-Erebus'
-
+parser = argparse.ArgumentParser()
+parser.add_argument('--model', type=str, help='Name of the model to load by default')
+args = parser.parse_args()
 loaded_preset = None
+available_models = sorted(set(map(lambda x : x.split('/')[-1].replace('.pt', ''), glob.glob("models/*[!\.][!t][!x][!t]")+ glob.glob("torch-dumps/*[!\.][!t][!x][!t]"))))
 
 def load_model(model_name):
     print(f"Loading {model_name}...")
@@ -85,7 +81,24 @@ def generate_reply(question, temperature, max_length, inference_settings, select
 
     return reply
 
+# Choosing the default model
+if args.model is not None:
+    model_name = args.model
+else:
+    if len(available_models == 0):
+        print("No models are available! Please download at least one.")
+        exit(0)
+    elif len(available_models) == 1:
+        i = 0
+    else:
+        print("The following models are available:\n")
+        for i,model in enumerate(available_models):
+            print(f"{i+1}. {model}")
+        print(f"\nWhich one do you want to load? 1-{len(available_models)}\n")
+        i = int(input())-1
+    model_name = available_models[i]
 model, tokenizer = load_model(model_name)
+
 if model_name.startswith('gpt4chan'):
     default_text = "-----\n--- 865467536\nInput text\n--- 865467537\n"
 else:
@@ -98,7 +111,7 @@ interface = gr.Interface(
         gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Temperature', value=0.7),
         gr.Slider(minimum=1, maximum=2000, step=1, label='max_length', value=200),
         gr.Dropdown(choices=list(map(lambda x : x.split('/')[-1].split('.')[0], glob.glob("presets/*.txt"))), value="Default"),
-        gr.Dropdown(choices=sorted(set(map(lambda x : x.split('/')[-1].replace('.pt', ''), glob.glob("models/*") + glob.glob("torch-dumps/*")))), value=model_name),
+        gr.Dropdown(choices=available_models, value=model_name),
     ],
     outputs=[
          gr.Textbox(placeholder="", lines=15),